556 results about "Time–frequency analysis" patented technology

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention discloses an intelligent primary and secondary fusion pole-mounted circuit breaker fault monitoring method, relates to the technical field of power equipment monitoring, and is used for solving the problem of insufficient real-time performance of fault monitoring under complex environment interference. According to the invention, electrical, mechanical vibration and environmental data are synchronously acquired through a multi-mode sensor array, and a timestamp synchronization mechanism is applied; performing multi-source interference classification on the original data, separating noise by using wavelet transform, and identifying interference types by clustering; kalman filtering adaptive compensation is applied based on an interference result, and environment feedback is introduced to ensure low delay; extracting multi-dimensional fault features, and forming a robust matrix through time-frequency analysis and principal component dimensionality reduction; inputting a deep learning model to carry out space-time modeling and rapid classification; a response mechanism is triggered to execute isolation or alarm, and closed-loop optimization is formed. The method effectively solves the problem of insufficient real-time performance under the interference of a complex environment, and improves the monitoring precision and the response speed.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention discloses an on-orbit spacecraft attitude estimation method and system based on ISAR image feature selection, and belongs to the field of aerospace control systems. The method comprises the following steps: acquiring an ISAR image of a spacecraft, and acquiring a complex linear structure set and three-dimensional feature points of an on-orbit spacecraft; then, according to the obtained three-dimensional-two-dimensional projection model, the CRLB of each reference structure in the complex linear structure set is deduced to carry out attitude estimation error analysis; calculating the trace of the CRLB covariance matrix of each reference structure to select an optimal feature structure, correcting the scattering point trace of the optimal feature structure by using polynomial fitting, and switching the reference structures as a new optimal feature structure according to the scattering point loss rate and a preset sequence; and optimizing the spacecraft attitude angle solving function by using a particle swarm and LM hybrid algorithm to obtain attitude angle parameters. The target with high-precision target attitude real-time estimation can be completed aiming at the problems that high-order frequency change in a dynamic environment is difficult to capture and resolution and noise suppression are contradictory due to a fixed window time-frequency analysis method.

The invention discloses an excitation load grounding fault detection method, system and device based on EEMD and Hilbert spectrum analysis and a medium, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: collecting and preprocessing an electric signal, and constructing a discretization time domain data sequence; performing iterative decomposition on the sequence to obtain multiple groups of components; time domain structure analysis is carried out, and target components containing fault mutation features are screened; frequency domain transformation is carried out on the target component to construct spectrum distribution, and normalization processing is carried out; and then time-frequency joint transformation is carried out, energy distribution and energy entropy indexes are calculated, and diagnosis is completed. According to the method, modal aliasing is suppressed by using EEMD adaptive noise injection and a set average strategy, accurate separation of high-frequency transient disturbance and low-frequency harmonic waves is realized, multi-dimensional feature combined diagnosis is formed by combining frequency domain screening and time-frequency analysis, and the fault detection precision and reliability are improved.

The invention discloses an insulator leakage current non-contact measurement method and system based on magnetic field sensing, and belongs to the technical field of electrician measurement, and the method comprises the steps: collecting original magnetic field data and environmental parameter data; performing interference stripping processing on the original magnetic field data based on the environmental parameter data to generate corrected magnetic field data; performing time-frequency analysis and noise suppression processing on the corrected magnetic field data to obtain leakage current characteristic data; performing inversion calculation by using the leakage current characteristic data to obtain a leakage current value; in combination with historical leakage current data, executing state evaluation processing to generate an insulator state; and according to the insulator state, automatic optimization processing is executed, and magnetic field sensor array gain and interference stripping processing parameters are adjusted. According to the invention, the technical scheme of multi-source sensing data fusion, interference stripping, physical inversion calculation and state evaluation closed-loop feedback optimization is adopted, and high-precision non-contact measurement and intelligent state evaluation of the leakage current of the insulator can be realized.

The invention relates to the technical field of elevator operation and maintenance, and discloses a multi-source data fusion method and system for elevator fault localization, and the method comprises the steps: carrying out the noise filtering and data alignment of multi-source monitoring data of an elevator during operation, and obtaining a monitoring data set; performing time-frequency analysis on acceleration sensor data in the monitoring data set to obtain vibration mode characteristics; performing harmonic analysis on the motor current data to obtain current distortion characteristics; performing space-time correlation on the vibration mode characteristics, the current distortion characteristics and the safety loop state in the monitoring data set to obtain a comprehensive fault characteristic vector; according to the comprehensive fault feature vector, specific components and types when the elevator breaks down are determined, so that fault positioning information is obtained; the fault positioning information is transmitted to an elevator monitoring terminal, and the elevator is maintained according to the fault positioning information; according to the invention, the fault positioning efficiency can be improved.

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention relates to a solid waste treatment facility monitoring and early warning method and related equipment, and the method comprises the following steps: carrying out the real-time physical parameter collection of a solid waste treatment facility through a sensing array, and obtaining a facility operation original data flow; performing time-frequency analysis on the facility operation original data stream to obtain operation state data; performing failure mode matching and risk factor extraction on the solid waste treatment facility based on the operation state data to obtain a failure feature matrix and a risk factor feature matrix; performing risk grade division on the solid waste treatment facility based on the failure characteristic matrix and the risk factor characteristic matrix to obtain a risk grade quantification sequence; and generating an early warning execution strategy of the solid waste treatment facility based on the risk level quantification sequence. The technical problem that a traditional monitoring means depending on manual inspection and threshold alarm often lags in response is solved.

The invention relates to the technical field of defect detection, and discloses a method for detecting defects of live cable equipment by using a high-frequency current detection method, which comprises the following steps of: exciting the live cable equipment to generate a high-frequency current signal, and judging the optimal frequency of the high-frequency current signal by adopting the minimum signal propagation loss; the method comprises the following steps: acquiring a current signal on cable equipment through a high-frequency current sensor, and performing multi-scale time-frequency analysis on the signal through wavelet packet transformation; carrying out noise reduction processing on the converted signal to remove an interference signal and extract a partial discharge signal; positioning the partial discharge signal based on the Bayesian theorem, and judging the position of a partial discharge source; and classifying the partial discharge signals by adopting a support vector machine. The excitation frequency of the high-frequency current signal enables the signal transmission loss to be minimized and the extraction efficiency of the partial discharge signal to be improved, and the signal transmission efficiency is improved in a complex cable environment, so that the defect detection precision and sensitivity are enhanced.

The invention discloses a force control joint dynamic error compensation method based on Kalman filtering, and the method comprises the steps: collecting and preprocessing multi-source signal data, and forming a force control joint input data set; constructing an energy hierarchical Kalman filtering model, executing energy constraint correction and outputting state estimation; calculating a prediction residual error, carrying out time-frequency analysis, adjusting a compensation gain, and generating residual error information; torque and angular velocity signals are extracted, a semantic state is recognized, and semantic gating parameters are output; fusing the state estimation, the residual information and the semantic parameters to generate a dynamic compensation instruction signal; energy layer and residual information changes are monitored, self-calibration is triggered, and a feedback closed loop is formed. According to the invention, by introducing energy hierarchical Kalman filtering, time-frequency modulation compensation and a semantic gating feedback mechanism, dynamic error self-adaptive accurate compensation of the force control joint in a complex multi-disturbance environment is realized.

The invention belongs to the technical field of intelligent monitoring, and discloses a typhoon safety evaluation system and method for an ocean engineering structure. The method comprises the steps of collecting original data, preprocessing the original data to obtain standard data, and performing time-frequency analysis on the standard data to obtain time-frequency characteristics; performing analysis based on the time-frequency characteristics to obtain modal parameters; analyzing according to the modal parameters to obtain a non-linear state judgment result; carrying out damage positioning according to a non-linear state judgment result; carrying out residual safety life prediction according to a damage positioning result; the safety guarantee level of the ocean engineering structure in the typhoon period is remarkably improved.

The invention discloses a broadband oscillation characterization platform of a multi-chip power module and a near-field characteristic evaluation method, and belongs to the technical field of power electronics. The continuous operation condition of the power converter is decoupled into a series of discrete switching points, transient near-field radiation data of a to-be-tested power module under the discrete switching points are obtained through a double-pulse test, and near-magnetic field radiation signals are reconstructed; performing time-frequency analysis on the reconstructed near magnetic field radiation signal to generate a two-dimensional time-frequency matrix; and calculating a radiation energy entropy value based on the two-dimensional time-frequency matrix, fusing the radiation energy entropy value with the peak amplitude in the two-dimensional time-frequency matrix to generate a comprehensive EMI index for evaluating the broadband oscillation electromagnetic interference risk, and triggering EMI risk early warning when the comprehensive EMI index exceeds a threshold value. According to the method, the spectrum complexity is quantified through the radiation energy entropy, the multi-dimensional EMI risk assessment is carried out by fusing the peak amplitude of the time-frequency matrix, and the broadband oscillation in the multi-chip parallel power module can be accurately and efficiently represented.

The invention provides an error modeling method and system based on a CCD camera alignment assembly system, and relates to the technical field of high-precision electronic manufacturing assembly, and the method comprises the steps: obtaining internal parameters and external parameters of a CCD camera through calibration, building a camera and mechanical coordinate system conversion relation, and constructing a collaborative calibration error tree; controlling the motion platform to move, synchronously acquiring pose and environment temperature data, decomposing vibration and thermal deformation error components through time-frequency analysis, and superposing the vibration and thermal deformation error components into dynamic errors; extracting a positioning error based on the pose data, and constructing a mechanical-optical coupling error spectrum in combination with the distortion parameters; and finally, fusing the collaborative calibration error tree, the dynamic error and the coupling error atlas to generate a global error atlas, and completing alignment assembly system error modeling based on the CCD camera. According to the invention, the precision and applicability of error modeling are improved.

The invention discloses a frequency hopping signal tracking interference method and device based on time-frequency diagram binaryzation, and the method comprises the steps: collecting a data stream of a target signal in real time, carrying out the data preprocessing of the data stream according to the working frequency band of the target signal, and obtaining narrowband IQ complex data; performing time-frequency analysis on the narrowband IQ complex data to obtain a corresponding time-frequency graph, and performing binarization processing on the time-frequency graph to obtain a binarized time-frequency matrix; and for each frequency point of the time-frequency matrix, detecting a frequency hopping signal by using a double sliding window, if the frequency hopping signal is detected, estimating a corresponding frequency hopping parameter, and then generating an interference signal according to the frequency hopping parameter and outputting the interference signal. According to the invention, tracking interference of the frequency hopping signal can be realized with fewer resources.

The invention relates to a precise health monitoring method based on multi-source data fusion. The method comprises the following steps of 1, collecting multi-source data; step 2, data preprocessing; step 3, multi-source data fusion; 4, health analysis and decision making; step 5, dynamic weight adjustment; and step 6, feedback optimization. The hierarchical fusion strategy is adopted to process heterogeneous data, a machine learning algorithm is combined to establish an individualized health model, physiological state changes are tracked in real time, a targeted intervention scheme is generated, and in terms of technical implementation, the system supports collaborative decision making of various analysis models, including rule-based behavior reasoning, time sequence feature analysis and environment threshold judgment, and the system has the advantages of being simple in structure and convenient to use. The contribution degrees of information of different sources are balanced through an adaptive weight distribution mechanism, time-frequency analysis and dimension reduction technologies are fused in the feature extraction process, the dynamic mode of the health state is effectively captured, cross validation optimization is adopted in model training, and the reliability of an evaluation result is ensured.

The invention discloses a wind turbine generator rotating speed extraction method and device and a wind turbine generator. The method comprises the steps that CMS vibration signals of the wind turbine generator are preprocessed; the method comprises the following steps: firstly, carrying out time-frequency analysis on a preprocessed vibration signal based on an STCFT algorithm, carrying out segmented approximation on the signal by adopting a linear frequency modulation basis function, then optimizing the frequency modulation frequency of the STCFT algorithm in each analysis window through a particle swarm optimization algorithm, and taking the minimum Renyi entropy value of the frequency spectrum of each analysis window as an optimization target, so as to optimize the frequency modulation frequency of the STCFT algorithm in each analysis window. Integrating the optimized frequency spectrums of the windows to obtain a time-frequency spectrum of the vibration signal; defining an initial search point and a search bandwidth of a target ridge line on a time-frequency spectrum, setting a dynamic bandwidth to limit a frequency search range at each moment, constructing a cost function to balance the smoothness of the ridge line and a local energy peak value of a frequency point, solving a target frequency point at each moment by minimizing the cost function, and fitting discrete target frequency points to obtain a target rotating speed curve. The rotating speed of the wind turbine generator can be accurately extracted.

A power transmission tower grounding resistance non-contact measurement method based on the electromagnetic coupling principle comprises the following steps that transmitting and receiving electromagnetic coupling coils are arranged around an iron tower grounding body, and geometric calibration and spatial positioning of a measurement area are completed by combining grounding grid structure parameters and soil conduction characteristics; injecting a high-frequency alternating-current excitation signal into the transmitting coil, and synchronously acquiring the amplitude and phase response of the induced voltage at a receiving coil end; carrying out filtering processing on the induction signal by adopting a time-frequency analysis and phase decoupling algorithm, and solving a function relationship between electromagnetic response and grounding impedance by combining a coupling equivalent model; based on experimental calibration data and scene parameters, a nonlinear mapping model of induction response and grounding resistance is constructed, and real-time inversion calculation is carried out by combining a solving result obtained in the third step. According to the invention, the real-time accurate measurement of the grounding resistance under the conditions of no power failure and no wire breakage is realized, and the safety, the operation convenience and the anti-interference capability in a complex environment in the measurement process are obviously improved.

The embodiment of the invention provides a switch cabinet partial discharge quantum sensing monitoring device. The device is applied to the technical field of power equipment monitoring, and comprises a light path regulation and excitation module which is used for generating and beam-splitting detection laser of a quantum sensor coupled to each Rydberg atom; the quantum electromagnetic induction module acts on Rydberg atoms and a partial discharge electromagnetic field, and fluorescence or absorption spectrum characteristics of atomic energy level transition are changed; extracting a weak discharge signal, and converting the weak discharge signal into an analyzable electric signal; the signal processing and diagnosis module is combined with spatial information acquired by a quantum sensor of doydberg atoms in a distributed manner, and a time-frequency analysis and pattern recognition algorithm is adopted to extract discharge characteristic quantity; partial discharge positioning, type identification and insulation state evaluation are realized based on the parameters, and a real-time monitoring result and early warning information are output. In this way, through deep fusion of quantum sensing and advanced signal processing, a high-precision and high-reliability technical solution is provided for power equipment state monitoring.

The invention belongs to the technical field of computer data processing and analysis, and particularly discloses a landslide deformation monitoring data anomaly detection method and system based on real-time data, and the method comprises the steps: carrying out the automatic collection and carrying out the self-calibration and quality evaluation when key induction factors such as heavy rainfall occur, it is ensured that complete and accurate original information can be obtained at the moment where accurate data is most needed; data are deeply purified by means of deep learning, time-frequency analysis and the like, and an early warning threshold value is dynamically calculated in combination with a geomechanical model and historical data, so that an anomaly judgment standard can adapt to complex and changeable geological environment conditions in real time, and false alarm and missing alarm caused by dependence on a static or subjective threshold value are avoided; through an integrated landslide type identification and trend prediction module, crossing from data anomaly detection to disaster evolution situation awareness is realized. Through seamless linkage of an early warning mechanism and intelligent decision support, the efficiency and pertinence of emergency response are greatly improved.

The invention belongs to the technical field of geological analysis, and discloses a ground subsidence analysis method and device, and the method comprises the steps: matching a sensor layout strategy corresponding to a foundation subsidence risk and meteorological data of a monitoring region; acquiring land subsidence data of the monitoring area according to a sensor; performing time-frequency analysis on the land subsidence data to obtain a geoacoustic spectrogram corresponding to the land subsidence data; inputting the geoacoustic spectrogram into a preset neural network, and outputting a settlement semantic vector corresponding to the geoacoustic spectrogram; acquiring and analyzing historical land subsidence data, and constructing a subsidence rule corresponding to the historical land subsidence data; and inputting the settlement semantic vector and the settlement rule into a preset space-time analysis model, and predicting to obtain a future settlement trend of the monitored area. By using the method disclosed by the invention, the problem that all risks in a certain area cannot be checked when geological data is monitored based on local monitoring points can be solved, and the problem that the geological risk analysis efficiency is relatively low can be solved.

The invention relates to a high-speed aircraft transition identification method based on a flight test high-frequency vibration signal. The method comprises the following steps: selecting high-quality measurement point data suitable for analysis from flight test high-frequency vibration data as a vibration signal to be analyzed; performing time-frequency analysis on the to-be-analyzed vibration signal, outputting a time-frequency diagram, and representing the change condition of energy in a frequency domain by using a centroid frequency and an envelope difference of the centroid frequency; carrying out preliminary identification on the transition occurrence moment; decomposing a to-be-analyzed vibration signal into a plurality of intrinsic mode functions with different center frequencies through variational mode decomposition; quantizing the change condition of the energy of each modal component along with time; and aiming at the change condition of the energy of each modal component along with the time, the transition is identified according to the energy of the mode excited by the transition, and the energy sudden increase point is the moment when the transition begins to occur. According to the method, the transition occurrence time can be effectively identified under the condition that no hole is formed in the surface of the aircraft.

The invention discloses an intelligent sound equipment control method and system based on sound field adaptive adjustment, and relates to the technical field of sound equipment control, and the method comprises the steps: carrying out the time-frequency analysis and matching of a real-time audio stream after the interception of the real-time audio stream, and obtaining a real-time sound field template; positioning the real-time spatial pose coordinates of the user according to the binocular vision sensing data; carrying out sound wave propagation attenuation analysis in a listening area by taking the real-time spatial pose as a reference, and outputting a four-dimensional sound image spatial vector; performing space sound field mapping operation on the real-time sound field template by adopting the four-dimensional sound image space vector to obtain a real-time scene sound field; according to a space pose sequence obtained through camera skeleton time sequence tracking, sound field parameter pre-calculation based on trajectory prediction is executed, and low-delay updating of a real-time scene sound field is carried out according to a calculation result. The technical problem that the sound field cannot be adjusted in real time according to the user position and the environment change in the prior art is solved, and the technical effect of accurately and dynamically adjusting the sound field is achieved.

The invention discloses a pulling and lifting cable frequency detection and cable force measurement method and system based on a millimeter wave radar, and belongs to the field of pulling and lifting cable force measurement. The method comprises the steps that vibration signals are collected through the millimeter wave radar to obtain complex data streams, time-frequency analysis is conducted on the complex data streams, and an interference model is established; the method comprises the following steps: reconstructing a noise-reduced vibration signal by adopting adaptive filtering, extracting candidate vibration frequencies through power spectral density estimation and spectral peak detection, calculating corresponding stability scores, and obtaining a first verification factor used for evaluating wind disturbance and a second verification factor used for evaluating vibration frequency deviation; according to the method, through multi-source sensing data fusion and self-adaptive signal processing, complex environment interference is effectively overcome, non-contact high-precision cable force measurement is achieved, and the method has the outstanding advantages of being high in anti-interference capacity, high in measurement reliability and the like.

The invention relates to the technical field of analog current fault diagnosis, in particular to an analog current fault diagnosis method based on redundancy feature suppression, which comprises the following steps: acquiring voltage and current data of a circuit output position according to a circuit simulation model, and constructing a circuit fault state data set according to the voltage and current data; performing time-frequency analysis on the circuit fault state data set through discrete wavelet transform; performing redundant feature suppression processing on the hierarchical frequency domain component set to obtain a circuit fault state data feature set; a DCNN-BiLSTM model is constructed, the DCNN-BiLSTM model is trained according to the circuit fault state data feature set, and the DCNN-BiLSTM model meeting a preset fault diagnosis correct rate is output as an analog power fault diagnosis model; and obtaining target to-be-detected voltage and current data, and performing analog current fault diagnosis on the target to-be-detected voltage and current data according to the analog current fault diagnosis model to obtain a corresponding fault type classification result. According to the invention, redundant features can be effectively suppressed, and the accuracy of analog power supply fault diagnosis is remarkably improved.